Two-step Estimation Research Articles

Strategy, capabilities, and business group performance

PurposeThe purpose of this paper is to examine the relationships among choice of industry diversification, capabilities and business group performance, as well as to point out the potential concern about endogenous role of industry diversification.Design/methodology/approachUsing data from the top 100 business groups in Taiwan from TEJ database. This study uses Heckman’s two-step estimation procedure and contingency model to achieve unbiased results and examine our hypotheses.FindingsThe results of this study find that if business groups’ marketing or operational capabilities are strong they should adopt a high level of diversification strategy and if business groups’ R&D capability is strong they should adopt a low level one. The results of this study also show that the endogenous problem of industry diversification exists, and needs to be considered. Moreover, our finding confirms the importance of capability–strategy fit, which, in turn, can achieve better performance.Practical implicationsOn average, high industry diversification groups perform better than low industry diversification groups after controlling for endogeneity issues. Business groups can achieve better performance if their strategy choices match the capabilities they encounter. Managers should pay attention to strategy-capability fit issues. Specifically, they should review their organizational capabilities as well as check their strategies within firms.Originality/valueThis study is one of the first that attempts to explore the endogenous role of diversification strategy choices, and empirical examine strategy-capability fit on business group performance.

Robust two-step phase estimation using the Simplified Lissajous Ellipse Fitting method with Gabor Filters Bank preprocessing

We present the Simplified Lissajous Ellipse Fitting (SLEF) method for the calculation of the random phase step and the phase distribution from two phase-shifted interferograms. We consider interferograms with spatial and temporal dependency of background intensities, amplitude modulations and noise. Given these problems, the use of the Gabor Filters Bank (GFB) allows us to filter-out the noise, normalize the amplitude and eliminate the background. The normalized patterns permit to implement the SLEF algorithm, which is based on reducing the number of estimated coefficients of the ellipse equation, from five terms to only two. Our method consists of three stages. First, we preprocess the interferograms with GFB methodology in order to normalize the fringe patterns. Second, we calculate the phase step by using the proposed SLEF technique and third, we estimate the phase distribution using a two-steps formula. For the calculation of the phase step, we present two alternatives: the use of the Least Squares (LS) method to approximate the values of the coefficients and, in order to improve the LS estimation, a robust estimation based on the Leclerc’s potential. The SLEF method’s performance is evaluated through synthetic and experimental data to demonstrate its feasibility.

Super-Resolution Time of Arrival Estimation for a Symbiotic FM Radio Data System

A symbiotic FM radio data system (SRDS) refers to a kind of digital broadcasting whose signal co-exists with the FM in channel. The SRDS signal has a symmetrical split spectrum in the band of the host FM signal spectrum. Like the FM signal, this kind of broadcasting signal also has a wide range of ground coverage. When used as an alternative to the positioning, navigation and timing (PNT), it will be meaningful for intelligent artifacts in safety-of-life applications. However, SRDS signals not only have much narrower bandwidth than GNSS signals, but also multiple peaks of auto-correlation function, which poses a serious challenge to the time of arrival (ToA) estimation. In this paper, we propose a super-resolution ToA estimation algorithm for the SRDS signals. Specifically, the properties of the auto-correlation function of SRDS signal are deduced concretely, and a two-step ToA estimation algorithm for the SRDS signal is proposed accordingly. Simulations are conducted with different configurations of quantization bits, SNR, oversampling rates and multi-path. The results show that the meter-level ranging performance can be obtained in the single-path Gaussian channel, and the ranging performance degrades from tens of meters to a hundred meters in multi-path environments.

Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade

Vehicle mass and road grade information is important to improve the control capability and further intellectualization of vehicles. With the aim of real-time estimation of mass and grade without additional sensors, a two-step estimator is proposed in this paper. In the first-step estimator, the recursive least square with dual forgetting factors is used to estimate the vehicle mass with the consideration of the time-varying rolling friction coefficient and system error. In the second-step estimator, the road grade is estimated using an extended Kalman particle filter. Based on the data of CarSim/MATLAB co-simulation, the proposed approach has faster convergence rate and better tracking accuracy on the premise of meeting the real-time requirements by comparison with other estimation algorithms. The performance of the estimator is finally validated by the vehicle road test, and the results show that the mass and grade are estimated with great accuracy and robustness under different road conditions.

Robust Estimation of Probit Models with Endogeneity

Probit models with endogenous regressors are commonly used models in economics and other social sciences. Yet, the robustness properties of parametric estimators in these models have not been formally studied. In this paper, we derive the influence functions of the endogenous probit model’s classical estimators (the maximum likelihood and the two-step estimator) and prove their non-robustness to small but harmful deviations from distributional assumptions. We propose a procedure to obtain a robust alternative estimator, prove its asymptotic normality and provide its asymptotic variance. A simple robust test for endogeneity is also constructed. We compare the performance of the robust and classical estimators in Monte Carlo simulations with different types of contamination scenarios. The use of our estimator is illustrated in several empirical applications.

Impact of ownership structure on risk-taking behavior of South Asian banks

The implementation of an effective risk management policy is necessary for the survival and success of banks. Ownership structure changes the risk-taking behavior of banks. Therefore, we analyze the impact of the ownership structure on risk-taking behavior of banks in emerging markets (i.e., Pakistan, India, and Bangladesh). We take public, private and foreign ownership of banks in this study. We collect the data from 64 banks of selected countries from 2011 to 2018. We measure risk-taking as capital adequacy, leverage coverage ratio, non-performing loan ratio, and return volatility. We use two-step system dynamic panel estimation for analyzing the results. We find that public and private banks have significant relationship with the risk-taking of banks. Furthermore, public and private banks show more risk-taking behavior as compared to foreign banks in all selected countries.

Modeling the global nickel market with a triangular simultaneous equations model

The global nickel market is being modeled using a triangular simultaneous equations model. The model is constructed to analyze the different situations of the market development. The content and nature of the global nickel market is examined to identify the characteristics that are needed to build the model. In this paper, two-step nonparametric estimator for a triangular simultaneous equation model is employed. The diminished form and the corresponding residuals are estimated non-parametrically in the first step. The research includes the estimation of the structural equation using the nonparametric regression with the diminished form residuals that is considered as the second step. The estimator is derived with consistency and asymptotic normality outcomes as well as optimal convergence rates. Moreover, the model is used to analyze the responses of the market to structural changes.

A two-step approach to interval estimation for continuous-time switched linear systems

Abstract This paper proposes a two-step interval estimation method for continuous-time switched linear systems subject to unknown but bounded disturbance and measurement noise. We first use an L∞ norm-based approach to attenuate the effect of uncertainties in observer design. Then, based on the obtained observer, interval estimation can be achieved via analyzing the bounds of estimation error. The proposed method is intuitive and independent of cooperativity constraint, which is main restriction of interval observer theory. The performance of the proposed method is demonstrated through a numerical simulation.

Carrier frequency offset estimation for FM and symbiotic FM radio data system hybrid signal

The symbiotic FM radio data system (SRDS) is a radio data system that a specially designed OFDM signal co-lives with FM signal, which enables a significantly higher data rate than existing radio data systems. The cyclic prefix of the OFDM symbol has the same length as the OFDM body, which enables the analytic separation of the co-channel OFDM and FM signal at receiver side, utilizing the fact that the OFDM body and prefix is equal. In this work, we show that the OFDM body and prefix cannot be viewed as equal when there is sufficient carrier frequency offset (CFO). Thus, we propose a two-step CFO estimation algorithm for FM and SRDS hybrid signal. The first step estimates the coarse CFO by exploring the characteristics of the FM signal. Once the coarse CFO is removed, the residual CFO is small enough for FM and OFDM separation. The second step fine estimates CFO from the OFDM-only signal using its repeated PN structure after the separation. Detailed mathematical equations are formulated and simulation results are given. The results show that the proposed algorithm works fine with the simulation setup and has a final residual CFO less than 3.9Hz.

How do large commercial banks adjust capital ratios: empirical evidence from the US?

This research explores the balanced panel data to examine the level of capital adjustment for major insured commercial banks over the 2002-2018 period using a two-step GMM estimator. The findings show that the speed of adjustment of the large insured commercial banks is faster than that of non-financial companies. The results contribute to a slower average adjustment pace of a total capital ratio than the total risk-based capital and capital buffer ratios. The adjustment of capital is faster in the post-crisis period than during and before-crises era. The adequately capitalized banks adjust capital ratio faster than well-capitalized banks. In contrast, the under-capitalized banks adjust the total risk-based capital ratio and capital buffer ratio more quickly than that of others. The low liquid banks needed a higher time to restore equilibrium than high liquid banks. The results of this study have economic significance for policy implications and future regulations.

The Effects of Financial and Operational Hedging on Company Value: The Case of Malaysian Multinationals

This study examines the value effects of financial and operational hedging in a managed floating exchange rate regime with strict limitations on the trading of Malaysian Ringgit for a sample of 109 Malaysian multinationals from 2004 to 2018. Using Tobin’s Q as a proxy for company value, the two-step system GMM estimation results show that, on average, derivatives hedging creates a value premium range of 7.88-8.21% in the short-run, and 18.81-19.80% in the long-run. This value premium emerged both after controlling for non-operational foreign exchange profits (losses), and its two components: transaction and translation profits (losses). In contrast, foreign debt hedging, on average, creates a value discount range of 8.19-8.54% in the short-run and 12.70-13.12% in the long-run. No evidence shows value effect for operational hedging though. The positive value effect of derivatives hedging should motivate managers of Malaysian multinationals to hedge foreign currency exposure through derivatives and encourage policymakers to take steps in developing derivatives market and products. However, the negative value effect of foreign debt hedging indicates that it destroys value. This negative effect might reflect two potential causes; higher company risk due to FC debt financing, and improper hedging practices including high costs of hedging in the underdeveloped derivatives market. These potential causes need further empirical evaluations.

Positioning in a Multipath Channel Using OFDM Signals With Carrier Phase Tracking

In developing a high accuracy terrestrial radio navigation system, as a complement to a global navigation satellite system (GNSS), it is recognized that the performance of time delay estimation is proportional to, and thereby limited by, the signal bandwidth. Given a possibly narrow signal bandwidth, the central carrier phase can, alternatively, provide a better distance accuracy, though the central carrier phase cycle ambiguity should be resolved. In practice, the carrier phase may be perturbed by multipath. In this paper, considering an orthogonal frequency division multiplexing (OFDM) signal, we propose a two-step carrier phase estimation method to reduce the error introduced by multipath. First, the propagation delay of the LoS path is coarsely determined, then the carrier phase is estimated using the earlier determined coarse time delays. Furthermore, a positioning model only based on carrier phase estimates is presented in this paper. The proposed technique is evaluated by statistical analyses and a simulated OFDM-based terrestrial positioning system in different roadway multipath environments. The results show that the impact of multipath on carrier phase estimation can be largely mitigated, so that the carrier phase can be used for precise positioning. In addition, fixing the integer carrier phase cycle ambiguities can significantly reduce the time for the position solution to converge to high precision.

The Effect of Pandemics on Domestic Credit: A Cross-country Analysis

Using a panel of 140 countries covering the period 1996-2018, this paper examines how pandemics influence the lending behavior of banks. We take advantage of a new index developed by Ahir et al. (2020) which measures discussions about pandemics at the country level. The results of the fixed-effects and the two-step system GMM estimations reveal that pandemics significantly hamper domestic credit available to the private sector. The negative effect of pandemics on credit levels is observed to be more prevalent for the low-income & emerging economies and non-OECD countries. The findings can provide insights for predicting the effect of recent COVID-19 pandemic on the behavior of credit market participants.

Phillips Curve and Output Expectations: New Perspectives from the Euro Zone

We propose a family of copula-based multivariate distributions with g-and- h marginals. After studying the properties of the distribution, we develop a two-step estimation strategy and analyze via simulation the sampling distribution of the estimators. The methodology is used for the analysis of a 7-dimensional dataset containing 40,871 operational losses. The empirical evidence suggests that a distribution based on a single copula is not flexible enough, thus we model the dependence structure by means of vine copulas. We show that the approach based on regular vines improves the fit. Moreover, even though losses corresponding to different event types are found to be dependent, the assumption of perfect positive dependence is not supported by our analysis. As a result, the Value-at-Risk of the total operational loss distribution obtained from the copula-based technique is substantially smaller at high confidence levels, with respect to the one obtained using the common practice of summing the univariate Value-at-Risks.

Panel Data Quantile Regression for Treatment Effect Models

In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and proposes a parametric estimation based on the minimum distance method. However, when the dimensionality of the covariates is large, the minimum distance estimation using this process is computationally demanding. To overcome this problem, we propose a two-step estimation method based on the quantile regression and minimum distance methods. We then show the uniform asymptotic properties of our estimator and the validity of the nonparametric bootstrap. The Monte Carlo studies indicate that our estimator performs well in finite samples. Finally, we present two empirical illustrations, to estimate the distributional effects of insurance provision on household production and TV watching on child cognitive development.

Correlation coefficient-based measure for checking symmetry or asymmetry of a continuous variable with additive distortion

This paper studies how to estimate and test the symmetry of a continuous variable under the additive distortion measurement errors setting. The unobservable variable is distorted in a additive fashion by an observed confounding variable. In this paper, a direct plug-in estimation procedure and two-step direct plug-in estimation procedure for correlation coefficient are proposed to measure the symmetry or asymmetry of a continuous variable, and empirical likelihood based confidence intervals are constructed to test the symmetry of the unobserved variable. The asymptotic properties of the proposed estimators and test statistics are investigated. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators and test statistics. These methods are applied to analyze a real dataset for an illustration.

Oracally efficient estimation and testing for an ARCH model with trend

This article investigates an ARCH model with trend and proposes a two-step estimation method and develops an ARCH effect test. The trend is estimated by the linear B-spline method, and then the least squares estimation (LSE) and a Lagrange multiplier (LM) test based on the residuals are presented. Under mild conditions, it is shown that the proposed LSE and the test statistic enjoy the oracle properties; namely, they perform as well as if the true trend function were known and then removed to obtain the ARCH errors. Simulation studies assess the finite-sample performance of the LSE and the test statistic. An empirical example on the US gross private saving series is analyzed to illustrate the usefulness of our procedure.

Characteristics, exchange experience, and environmental efficiency of mayors: Evidence from 273 prefecture-level cities in China

The existing literature on environmental efficiency focuses on economic and social factors, and rarely on the role of local leadership. Thus, this study aims to measure the environmental efficiency and analyze the impact of mayors' characteristics on environmental efficiency. By using the metafrontier-stochastic frontier analysis two-step estimate method, the average value of the overall environmental efficiency in China is only 0.523, which is high in the eastern region and low in the central and western regions. Empirical results show that mayors' tenure plays an inverted U-shaped effect on environmental efficiency. Mayors who are highly educated, young, and aware of environmental protection can improve environmental efficiency. Moreover, mayors’ experience in official exchange can improve environmental efficiency; mayors with work experience in the central government are effective in terms of environmental governance. The policy implication is that improving the educational level of leaders, appointing young cadres, and strengthening official exchanges can effectively improve environmental efficiency.

Micrometer Accuracy Phase Modulated Radar for Distance Measurement and Monitoring

An enhanced accuracy random binary phase modulated radar is proposed. It can be used in high accuracy monitoring in manufacturing. Compared with the traditional high accuracy radar using frequency modulated continuous wave (FMCW), the proposed radar system can be used in a multi-user scenario without occupying more bandwidth. A two-step distance estimation method is introduced to estimate the distance. First, the distance estimation accuracy is narrowed down to a half carrier wavelength by analyzing the envelope of the phase modulated signal. Then the carrier phase information increases the distance accuracy to several micrometers. An equalization method is introduced to solve the I/Q imbalance problem. The proposed radar system is demonstrated at a carrier frequency of 80 GHz with a bandwidth of 2 GHz. The measured distance error was within ±7 $\mu $ m. In addition, a high measurement repetition rate of 500 kHz was reached which is suitable for real-time monitoring in automatic manufacturing.

Efficient estimation of heterogeneous coefficients in panel data models with common shocks

This paper investigates the estimation and inference issues of heterogeneous coefficients in panel data models with common shocks. We propose a novel two-step method to estimate the heterogeneous coefficients. We establish the asymptotic theory of our estimators, including consistency, asymptotic representation, and limiting distribution. Our two-step method can effectively address the limitations of the existing methods, such as the common correlated effects method proposed by Pesaran (2006) and the iterated principal components method proposed by Song (2013). The two-step estimator is as efficient as the two existing competitors in the basic model, and more efficient in the model with zero restrictions. Intensive Monte Carlo simulations show that the proposed estimator performs robustly in a variety of data setups.